{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YWTRZQJDRJYBZJMZRURAOOYNPB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ea5c95ed3c3c66339f4d1a13fabc33ec5ecf314121553e94d7145bec686c1d94","cross_cats_sorted":["econ.EM","stat.ME","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-09-14T18:29:45Z","title_canon_sha256":"511327b000d369d25e22b3bc811aa5b3048dedfab76516cebd8101c66ce1b59e"},"schema_version":"1.0","source":{"id":"2509.11381","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.11381","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"arxiv_version","alias_value":"2509.11381v3","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.11381","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"pith_short_12","alias_value":"YWTRZQJDRJYB","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"pith_short_16","alias_value":"YWTRZQJDRJYBZJMZ","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"pith_short_8","alias_value":"YWTRZQJD","created_at":"2026-06-05T00:13:43Z"}],"graph_snapshots":[{"event_id":"sha256:d3092b06225f8802470b7ebeecf768cff2aa3754bfe6dd49f7da9d43d2ca3c4b","target":"graph","created_at":"2026-06-05T00:13:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2509.11381/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recursive decision trees are widely used to estimate heterogeneous causal treatment effects in experimental and observational studies. These methods are typically implemented using CART-type recursive partitioning, with splitting criteria designed to identify variation in treatment effects across covariate-defined subgroups. We study causal tree estimators based on adaptive recursive partitioning and establish lower bounds on their estimation accuracy. The class we analyze includes versions with and without sample splitting, based on common treatment effect and squared-error splitting criteria","authors_text":"Jason M. Klusowski, Matias D. Cattaneo, Ruiqi Rae Yu","cross_cats":["econ.EM","stat.ME","stat.ML","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-09-14T18:29:45Z","title":"Accuracy Limits of Causal Trees for Individualized Treatment Effects"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.11381","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9429c4a0fb96fc58776224161ed95fdf4d187c7bc91987617c1873c0b9292d2c","target":"record","created_at":"2026-06-05T00:13:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ea5c95ed3c3c66339f4d1a13fabc33ec5ecf314121553e94d7145bec686c1d94","cross_cats_sorted":["econ.EM","stat.ME","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-09-14T18:29:45Z","title_canon_sha256":"511327b000d369d25e22b3bc811aa5b3048dedfab76516cebd8101c66ce1b59e"},"schema_version":"1.0","source":{"id":"2509.11381","kind":"arxiv","version":3}},"canonical_sha256":"c5a71cc1238a701ca5998d22073b0d7860d68ef687a980a16af58b0d2d69b2ec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c5a71cc1238a701ca5998d22073b0d7860d68ef687a980a16af58b0d2d69b2ec","first_computed_at":"2026-06-05T00:13:43.847070Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-05T00:13:43.847070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1SXIo0M0E9rW153TZYQJXTPZ2Z6CJ3YjOqICzLBTOVIO7q1+/F0ZkeBOJshoQ5nWvQLB400J4cVlz67fkChPDw==","signature_status":"signed_v1","signed_at":"2026-06-05T00:13:43.847732Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.11381","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9429c4a0fb96fc58776224161ed95fdf4d187c7bc91987617c1873c0b9292d2c","sha256:d3092b06225f8802470b7ebeecf768cff2aa3754bfe6dd49f7da9d43d2ca3c4b"],"state_sha256":"4139a4bcb086f69fc1d892ed56bc69379337818806bdfee465cad74734a94b66"}